from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-12-20 14:07:27.483186
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'1. Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64('2020-12-06'),
'red', 'inside top left'),
'2. Soft Lockdown': (np.datetime64('2020-12-06'), np.datetime64(TODAY),
'orange', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Sun, 20, Dec, 2020
Time: 14:07:31
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -43.8385
Nobs: 146.000 HQIC: -44.9304
Log likelihood: 1559.98 FPE: 1.45640e-20
AIC: -45.6777 Det(Omega_mle): 8.02296e-21
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.461317 0.170100 2.712 0.007
L1.Burgenland 0.143560 0.083582 1.718 0.086
L1.Kärnten -0.235395 0.067501 -3.487 0.000
L1.Niederösterreich 0.104776 0.199938 0.524 0.600
L1.Oberösterreich 0.250392 0.167495 1.495 0.135
L1.Salzburg 0.177805 0.086402 2.058 0.040
L1.Steiermark 0.088140 0.121207 0.727 0.467
L1.Tirol 0.147113 0.079726 1.845 0.065
L1.Vorarlberg 0.007128 0.077583 0.092 0.927
L1.Wien -0.127258 0.162860 -0.781 0.435
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.549082 0.222021 2.473 0.013
L1.Burgenland 0.009956 0.109095 0.091 0.927
L1.Kärnten 0.363867 0.088104 4.130 0.000
L1.Niederösterreich 0.130762 0.260967 0.501 0.616
L1.Oberösterreich -0.204890 0.218622 -0.937 0.349
L1.Salzburg 0.197090 0.112775 1.748 0.081
L1.Steiermark 0.238874 0.158204 1.510 0.131
L1.Tirol 0.142895 0.104061 1.373 0.170
L1.Vorarlberg 0.187752 0.101264 1.854 0.064
L1.Wien -0.596614 0.212571 -2.807 0.005
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.303094 0.073568 4.120 0.000
L1.Burgenland 0.103471 0.036149 2.862 0.004
L1.Kärnten -0.025388 0.029194 -0.870 0.384
L1.Niederösterreich 0.075425 0.086473 0.872 0.383
L1.Oberösterreich 0.289934 0.072441 4.002 0.000
L1.Salzburg -0.000545 0.037369 -0.015 0.988
L1.Steiermark -0.029732 0.052422 -0.567 0.571
L1.Tirol 0.089776 0.034481 2.604 0.009
L1.Vorarlberg 0.131371 0.033554 3.915 0.000
L1.Wien 0.069766 0.070436 0.990 0.322
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.183919 0.085446 2.152 0.031
L1.Burgenland -0.008520 0.041986 -0.203 0.839
L1.Kärnten 0.022247 0.033907 0.656 0.512
L1.Niederösterreich 0.014517 0.100435 0.145 0.885
L1.Oberösterreich 0.414002 0.084138 4.921 0.000
L1.Salzburg 0.098468 0.043402 2.269 0.023
L1.Steiermark 0.195604 0.060886 3.213 0.001
L1.Tirol 0.030425 0.040048 0.760 0.447
L1.Vorarlberg 0.102516 0.038972 2.630 0.009
L1.Wien -0.055130 0.081809 -0.674 0.500
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.612205 0.178812 3.424 0.001
L1.Burgenland 0.073761 0.087863 0.840 0.401
L1.Kärnten 0.004792 0.070958 0.068 0.946
L1.Niederösterreich -0.068363 0.210178 -0.325 0.745
L1.Oberösterreich 0.139663 0.176074 0.793 0.428
L1.Salzburg 0.044977 0.090827 0.495 0.620
L1.Steiermark 0.121555 0.127415 0.954 0.340
L1.Tirol 0.218288 0.083809 2.605 0.009
L1.Vorarlberg 0.016946 0.081557 0.208 0.835
L1.Wien -0.146088 0.171201 -0.853 0.393
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.178196 0.123957 1.438 0.151
L1.Burgenland -0.032347 0.060909 -0.531 0.595
L1.Kärnten -0.014902 0.049190 -0.303 0.762
L1.Niederösterreich 0.157559 0.145702 1.081 0.280
L1.Oberösterreich 0.401029 0.122060 3.286 0.001
L1.Salzburg -0.024719 0.062964 -0.393 0.695
L1.Steiermark -0.040739 0.088327 -0.461 0.645
L1.Tirol 0.188987 0.058099 3.253 0.001
L1.Vorarlberg 0.036213 0.056537 0.641 0.522
L1.Wien 0.160770 0.118681 1.355 0.176
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.194425 0.156683 1.241 0.215
L1.Burgenland 0.076724 0.076989 0.997 0.319
L1.Kärnten -0.042127 0.062176 -0.678 0.498
L1.Niederösterreich -0.038481 0.184167 -0.209 0.834
L1.Oberösterreich -0.109480 0.154284 -0.710 0.478
L1.Salzburg 0.012473 0.079587 0.157 0.875
L1.Steiermark 0.390646 0.111646 3.499 0.000
L1.Tirol 0.516978 0.073437 7.040 0.000
L1.Vorarlberg 0.224081 0.071463 3.136 0.002
L1.Wien -0.224044 0.150014 -1.493 0.135
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.095714 0.180388 0.531 0.596
L1.Burgenland 0.030664 0.088638 0.346 0.729
L1.Kärnten -0.115137 0.071583 -1.608 0.108
L1.Niederösterreich 0.174947 0.212032 0.825 0.409
L1.Oberösterreich 0.020587 0.177627 0.116 0.908
L1.Salzburg 0.225959 0.091628 2.466 0.014
L1.Steiermark 0.152301 0.128538 1.185 0.236
L1.Tirol 0.087926 0.084548 1.040 0.298
L1.Vorarlberg 0.038257 0.082276 0.465 0.642
L1.Wien 0.299204 0.172711 1.732 0.083
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.581535 0.100408 5.792 0.000
L1.Burgenland -0.017323 0.049338 -0.351 0.725
L1.Kärnten 0.000030 0.039845 0.001 0.999
L1.Niederösterreich -0.034949 0.118021 -0.296 0.767
L1.Oberösterreich 0.286274 0.098871 2.895 0.004
L1.Salzburg 0.010467 0.051002 0.205 0.837
L1.Steiermark 0.009290 0.071547 0.130 0.897
L1.Tirol 0.076590 0.047061 1.627 0.104
L1.Vorarlberg 0.180585 0.045796 3.943 0.000
L1.Wien -0.087130 0.096135 -0.906 0.365
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.131325 -0.012629 0.193076 0.241663 0.034454 0.090769 -0.120297 0.150501
Kärnten 0.131325 1.000000 -0.024692 0.182344 0.125890 -0.160991 0.169378 0.021279 0.296342
Niederösterreich -0.012629 -0.024692 1.000000 0.259109 0.062274 0.197249 0.092274 0.026237 0.355015
Oberösterreich 0.193076 0.182344 0.259109 1.000000 0.273400 0.274112 0.092948 0.054108 0.079494
Salzburg 0.241663 0.125890 0.062274 0.273400 1.000000 0.139932 0.063476 0.071248 -0.037061
Steiermark 0.034454 -0.160991 0.197249 0.274112 0.139932 1.000000 0.094810 0.067895 -0.163974
Tirol 0.090769 0.169378 0.092274 0.092948 0.063476 0.094810 1.000000 0.131640 0.119547
Vorarlberg -0.120297 0.021279 0.026237 0.054108 0.071248 0.067895 0.131640 1.000000 0.076416
Wien 0.150501 0.296342 0.355015 0.079494 -0.037061 -0.163974 0.119547 0.076416 1.000000